Description Where: EcoSciences Precinct, Dutton Park, Brisbane When: Monday 3rd June to Friday 7th June, 2024 Who should attend: Both new and experienced modellers will benefit from the course, as well as anyone who would simply like to understand the theory and practice behind simulation-based data processing and environmental forecasting. Topic: “PEST” refers to a software package and […]
PEST Course: Brisbane Read More »
Week 1 NZ/AUS https://vimeo.com/866196071%20 This is the first recording from the NZ/AUS self-paced guided study course on Decision Support Groundwater Modeling with Python. Week 1 covered the Freyberg model, Bayes equation, and the PEST interface.Presenters: Jeremy White, Rui Hugman and Brioch Hemmings USA https://vimeo.com/866312617%20 This is the first recording from the USA self-paced guided study
Live session recordings Read More »
GMDSI and the USGS have co-funded the development of a series of jupyter notebooks that use python scripting to demonstrate many aspects of applied decision-support groundwater modelling, from introductions to concepts through to complete modelling workflows. This webinar presented and discussed the design and content of these notebooks, including how to get started using them,
Webinar: Applied decision support groundwater modelling with python Read More »
Using the PLPROC parameter preprocessor supplied with the PEST suite, moveable polylinear and polygonal structural features such as faults and aquitard windows can be inserted into a model. These features can be assigned to one or many model layers. Hydraulic properties can vary along and within them. If appropriate, the hydraulic properties associated with structural
Structural Overlay Parameters and PLPROC Read More »
Construction of a decision support groundwater model requires that parameters be adjustable, stochastic and representative of geological conditions. This can be difficult to achieve in complex hydrogeological environments where controls on hydraulic properties are both geological and structural in origin.This worked example report demonstrates use of the PLPROC parameter preprocessor in construction of a stochastic
Worked Example: Structural Overlay Parameters in a Tunnel Model Read More »
Date: 9th May 2023 (Recorded – see button below) John Doherty, Jeremy White and Catherine Moore presented issues that occupy the boundaries between uncertainty analysis and decision-making/policy-formulation. They discussed some of the problems that beset the making of decisions in an uncertain world. The talks are non-technical; the issues are important. They included: Uncertainties in
So we’ve ticked the uncertainty box. What happens next? Read More »
Date: 28th February 2023 Webinar recording This webinar had two presenters. The first is Chris Li (from CDM Smith). Chris delivered a talk entitled ‘The “Cinderella Syndrome” of groundwater modelling, and overcoming it through risk-orientated uncertainty analysis’ at the recent Australian Groundwater Conference. It was highly acclaimed. So we asked him to make his talk a little
Demystifying uncertainty and its policy repercussions Read More »
This tutorial introduces data space inversion (DSI). DSI can be used to explore the uncertainties of predictions made by complex models with complicated hydraulic property fields. The model run burden is extremely low, and unrelated to the complexity of the complex model’s construction or parameterisation. There is considerable overlap between this tutorial and the “Four
Data Space Inversion Read More »
This tutorial explains four ways to explore the uncertainties of two predictions made by a relatively simple, fast-running model. These are: Linear analysis Sampling a linearised posterior covariance matrix Iterative ensemble smoother Data space inversion In doing this tutorial, you get to use the following programs: PEST PEST_HP PESTPP-IES DSI1 Other members of the PEST
Four Ways to Explore Model Predictive Uncertainty Read More »
This is the last of a three-part series recorded by John Doherty. The first video is recalling what you once knew. The second video will be focussed on highly-parameterised inversion and the third video will be on uncertainty analysis.These videos will give you a good understanding of the mathematical concepts that underpin model calibration and uncertainty
Uncertainty Analysis Read More »
GMDSI is managed by the National Centre for Groundwater Research and Training (NCGRT) and administered by Flinders University.
